Research Of Motor Imagine Classification Algorithm Based On EEG

The various kinds of brain illness and illness of neural system make brain science become the most challenging research in the 21^st century. Many neuromuscular disorders can disrupt the channels through which the brain communicates with and controls its external environment, which usually deprive patients; of their basic movement function and normal communication. The common channel responded to environment is sometimes useless to them. So the research on brain science has attracted more attention to satisfy the demands of the people who want to overcome their disadvantages by the brain science research and the related domains. In recent years, with the rapid development of electric and computer technology, the foundation of EEG processing has been built. And the research of Brain Computer Interface (BCI) also becomes a hot item.In the research of BCI, the BCI system based on electroencephalogram gets much attention because of its characteristic of simplicity, safety and non-injury. In this paper, the BCI system based on imaginary motor is researched. The purpose of this thesis is to investigate BCI system based on spontaneous electroencephalographic signals, identify particular mental tasks, and form the control dictation. So the commutation between the brain and the computer can be come true and those persons who are seriously handicapped can get help. This kind of BCI system would be a promising one because of the simple structure, easy and non-injury EEG signals, no stimulation equipment, short training processing, etc. At the same time, the spontaneous electroencephalographic signals that are collected from scalp are very weak and the signal-to-noise of signals is low. So the valid method of electroencephalographic signal processing is a key technology in the research of BCI. In the foundation of the past work, this paper focuses on some topics described as following. 1) The design of information collected.Information collected model is created to collected the electroencephalographic signals. A new experiment is designed to record the EEG according to the requirement of the subject on the basis of the EEG record equipment and the former software in the laboratory. And then a software is devised using VC++6.0 to meet the requirements of the experiment.2) Pre-processing of EEG signalsEEG signals are very weak and are always influenced by eye movements, blinks and muscle, heart and power line noise. And so it is necessary to remove the artifacts and noised in EEG. In this paper, wavelet transformation and digital filter are applied to remove the artifacts. The excellent result has been achieved.3) Facture extraction of the EEG signalsIn BCI system, it is very important to find a meaningful EEG signal feature that contains the remarkable information of different mental actions. The adaptive autoregressive model combined with the event-related desynchronization is used in our research to obtain one feature. The wavelet entropy is treated as the other feature in our approach. So the feature extraction of EEG signals is completed4) Design of the Mental Tasks ClassifierIt is necessary for a BCI system to design a classifier with an excellent performance. For this purpose, different approaches have been investigated in detail. Recent advances in machine learning research have pointed out the advantaged of support vector machine(SVM) over other classification techniques. Solid theoretical foundations, good generalization capabilities and easy parameters updating are among the most appealing qualities of SVM for BCI applications. In the experiment, classifiers adopt Fisher discrimination, RBF neural net work and support vector machine (SVM). SVM classifier achieves the highest accuracy comparing the others in the classification results.The dataset of 2003 international BCI competition are used for research purpose, and dataset recorded in our experiment are adopted for testing the algorithm. The classification accuracy using SVM on features extracted from EEG can reach 87.9% and 83.3%. The results show that ERD/ERS, wavelet entropy and SVM can be used as a classifier in BCI system.